Construction and evaluation of a prognostic model for breast cancer based on aging related genes.

Aging plays an important role in the development of breast cancer (BRCA); however, the specific underlying mechanism remains unclear. In this study, consensus clustering was initially used to classify BRCA subtypes. Thereafter, differentially expressed genes were identified within BRCA datasets and across subtypes, with overlapping genes being selected for further analysis. Cox regression analysis was used to identify key genes to construct a risk model for predicting prognosis. Furthermore, GSVA was used for pathway enrichment analysis, the infiltration levels of various immune cells were assessed, and the correlation between immune checkpoints and risk scores was analyzed. Finally, the expression levels of the key genes were validated through qRT-PCR. In addition, we also investigated the role of CXCL14 overexpression in the proliferation, invasion, and migration of MDA-MB-231 and MCF-7 cells. Human peripheral blood lymphocytes were co-cultured with MDA-MB-231 and MCF-7 cells at a cell density ratio of 2:1, and the proportion of live CD8+ T cells was measured by flow cytometry. Cox analysis revealed JCHAIN, KRT15, and CXCL14 as key prognosis-related genes; therefore, these genes were used to construct the risk model. In addition, age and stage were identified as independent prognostic factors. GSVA showed pathway enrichment in various risk groups, with the infiltration levels of 27 immune cell types being correlated with the risk score. The high-risk group exhibited downregulation of LAG3, CD274, CTLA4, and PDCD1. qRT-PCR validated the downregulation of JCHAIN and CXCL14 in a specific subgroup. CXCL14 overexpression significantly inhibited the proliferation, invasion, and migration capabilities of breast cancer cells. Furthermore, CXCL14 further suppressed tumor occurrence and development by activating CD8+ T cell-mediated immune mechanisms. In this study, the aging-related genes JCHAIN, KRT15, and CXCL14 were identified as key biomarkers for predicting the prognosis of BRCA.